SE- AI-Driven SRE & Cloud SRE

About Us

Responsibilities - AI-Driven Initiatives

1. AI-Augmented Incident Response & Root Cause Analysis

• Support incident response as a shadow or secondary responder, using AI-driven detection, correlation, and root-cause analysis tooling under the guidance of senior engineers to speed up triage.
• Help assemble incident timelines from metrics, logs, traces, and deploy history, learning to separate the alert that fired from the change that actually caused it.
• Help operate and monitor the existing AI SRE sub-agents (covering areas such as incident summarization, monitor-gap detection, and usage attribution), flagging anomalies or failures for senior review.
• Assist in documenting AI-assisted incident findings and postmortems, including tracking corrective and preventive actions through to closure.

2. AI-Driven Observability & Alert Quality

• Build and maintain monitors, dashboards, and SLO definitions in Datadog as code via Terraform, rather than clicking through the UI.
• Assist in reducing alert noise using AI/ML-assisted detection (anomaly, outlier, and forecast monitors, composite conditions, and dynamic thresholds) so that pages stay actionable and rare.
• Run recurring monitor-hygiene and coverage-gap reviews - no-data monitors, missing no-data notification, orphaned monitors on decommissioned services, and monitors with no owning team tag or valid notification target.
• Support SLI/SLO and error-budget reviews for critical customer journeys, escalating services trending toward budget exhaustion to senior team members.

3. AI-Enabled Cloud Reliability, Capacity & Cost Engineering

• Support day-to-day reliability of AWS workloads - ECS/Fargate, EKS, Lambda, RDS/Aurora, ALB, SQS/SNS and Step Functions - under senior guidance.
• Use predictive analytics and intelligent monitoring dashboards to spot capacity, saturation, and cost anomalies across host, container, log, APM, and serverless usage, escalating notable trends with evidence.
• Assist in observability and cloud cost governance, attributing spend and telemetry volume to owning teams and services and helping identify low-value, high-cost signals.

4. AI Governance, Risk & Reliability Reviews (Learning Track)

• Participate in architecture and design reviews, learning how reliability risk and AI-agent risk (e.g., prompt injection, authorization gaps, secrets exposure in agent and tool configuration, blast radius of autonomous actions) is assessed and documented.
• Learn how production-readiness, change management, and audit expectations apply in a regulated environment (PCI-DSS, SOC 2, SOX, GLBA), and why observability evidence matters to auditors.
• Assist in tracking action items from the AI-risk and reliability remediation roadmap and help prepare status updates for stakeholders.

5. AI-Enabled DevOps, CI/CD & Automation

• Support the implementation of reliability and safety controls within CI/CD pipelines, learning automation-first and AI-augmented approaches and shift-left practices from senior engineers.
• Help integrate AI-assisted checks (e.g., change-risk summaries, infrastructure drift detection, dependency and configuration checks) into the delivery lifecycle.
• Write and maintain automation in Python and Bash against platform APIs (Datadog, AWS, GitHub, PagerDuty, Jira) to replace recurring manual work, and contribute Terraform modules and pull requests under review.
• Assist with runbook creation and the progressive automation of runbook steps toward self-healing.

6. AI-Enabled Program & Workflow Support

• Support multiple concurrent reliability initiatives using AI-enabled workflow and tracking tools shared across SRE and Security teams.
• Help promote a reliability-first culture by learning and sharing data-driven, AI-backed operational practices with the wider team.

SRE & Cloud Capabilities

• Interest in observability and monitoring fundamentals - metrics, logs, traces, and APM - and a willingness to learn predictive analytics and anomaly detection.
• Exposure to at least one observability platform (Datadog preferred; Grafana/Prometheus, New Relic, CloudWatch, or ELK/OpenSearch also relevant).
• Foundational understanding of SLI, SLO, and error-budget concepts, and of why alert fatigue is a reliability problem rather than an annoyance.
• Hands-on exposure to AWS (or an equivalent hyperscaler) across compute, networking, storage, and managed database services, with interest in automated reliability tooling.
• Foundational knowledge of container and orchestration concepts (Docker, ECS or Kubernetes) and of serverless execution models.
• Beginner-to-intermediate familiarity with Infrastructure as Code (Terraform preferred; Ansible or CloudFormation acceptable) and an eagerness to build deeper IaC expertise.
• Basic Linux troubleshooting and networking fundamentals, including DNS, TLS, load balancing, timeouts and retries.
• Some hands-on exposure to automation or scripting (Python, Bash, or similar), including consuming REST APIs and parsing JSON.
• Comfort with Git and pull-request-based workflows, and exposure to CI/CD tooling (GitHub Actions, Jenkins, GitLab CI, ArgoCD or similar).
• Familiarity with incident management and on-call concepts, including severity models, escalation policies, and paging tools such as PagerDuty or Opsgenie.
• Basic exposure to cloud posture and continuous monitoring concepts, and to change and release management discipline.
• Practical curiosity about LLM-based assistants and agents applied to operations, and about how to verify whether their output is actually correct.

About You

• 5–9 years of experience in SRE, DevOps, cloud infrastructure, platform, or production-support engineering, with an eagerness to build deeper expertise in infrastructure as code.
• Some exposure to cloud-native logging and monitoring tools, and interest in AI-driven alerting and noise reduction.
• You debug from evidence: when something breaks, your instinct is to look at the data before offering a theory, and you are comfortable saying “I don’t know yet, here is what I am checking.”
• You would rather automate a task the second time you do it than the tenth.
• Coursework or hands-on exposure to monitoring and observability platforms.
• Exposure to CI/CD pipelines and interest in integrating reliability controls and shift-left practices.
• Basic understanding of capacity, performance, and reliability cost concepts, including risk-based prioritization of remediation work.
• Awareness of common compliance frameworks (PCI-DSS, SOC 2, SOX, HIPAA/GLBA) and comfort working with production-change discipline.
• Willingness to learn incident management processes, including AI-assisted triaging, and the judgment to escalate early rather than sit quietly on an uncertain production signal.
• Strong analytical and problem-solving skills, with curiosity to assess simple architectures for failure modes under senior guidance.
• Clear written communication - you can explain an incident, a metric, or a trade-off to someone who was not in the room.
• Interest in learning how reliability standards, operational policies, and governance frameworks are developed and maintained, including for AI-agent usage.

Core Competencies

• Site Reliability Engineering fundamentals (SLI/SLO, error budgets, toil reduction)
• Observability and Monitoring (metrics, logs, distributed tracing, APM, RUM/Synthetics)
• AIOps basics - anomaly detection, event correlation, predictive analytics, alert noise reduction
• Incident Response and Postmortem/RCA practice
• Cloud Infrastructure (AWS core services, multi-account and IAM basics)
• Containers and Orchestration (Docker, ECS, Kubernetes)
• Infrastructure as Code and Configuration Management (Terraform, Ansible)
• CI/CD, Release Engineering and Progressive Delivery
• Automation and Scripting (Python, Bash, platform APIs)
• Capacity, Performance and Reliability Cost Engineering (FinOps fundamentals)
• Resilience Patterns (retries, timeouts, circuit breakers, graceful degradation)
• Chaos/Failure-Injection and Disaster Recovery concepts
• Runbook Automation, Automated Remediation and Self-Healing (exposure)
• DevSecOps and secure-by-default operations fundamentals
• AI Agent Risk & Governance basics (authorization models, prompt-injection risk, secrets exposure in AI tool configs, human-in-the-loop controls)

Preferred Certifications

• AWS Certified Cloud Practitioner / Associate-level AWS certification (preferred, not required)
• HashiCorp Certified: Terraform Associate (preferred, not required)
• Datadog Fundamentals or an equivalent observability-platform certification (preferred, not required)
• Certified Kubernetes Administrator (CKA) or KCNA (preferred, not required)
• Exposure to AI/ML applications in operations and reliability is a plus (preferred, not required)